Preambule

Load data

## TQ doses in data:
## 
##   0  50 100 150 300 600 
## 186  55  57   1 481  57

Data summaries

## 
##      Brazil    Cambodia    Colombia    Ethiopia       India        Peru 
##         233          29          13          42          51         298 
## Philippines    Thailand     Vietnam 
##           4         138          29
## [1]  0.5476451 14.2857141
## Median number of methb measurements is 11
## [1]  2 17
## 
##   0   1 
## 543 294
##   Dose (mg) Rec 6 mths (%)     n
## 1         0           60.8 186.0
## 2        50           40.0  55.0
## 3       100           42.1  57.0
## 4       150            0.0   1.0
## 5       300           27.0 481.0
## 6       600            8.8  57.0

CYP2D6

##          
##             0 0.5   1 1.5   2
##   *1/*1     0   0   0   0 306
##   *1/*10    0   0   0  37   0
##   *1/*17    0   0   0  25   0
##   *1/*3     0   0   3   0   0
##   *1/*4     0   0  46   0   0
##   *1/*41    0   0   0  19   0
##   *1/*5     0   0  12   0   0
##   *1/*6     0   0   1   0   0
##   *1/*9     0   0   0   9   0
##   *10/*10   0   0  26   0   0
##   *10/*41   0   0   6   0   0
##   *17/*17   0   0   3   0   0
##   *17/*41   0   0   2   0   0
##   *3/*4     1   0   0   0   0
##   *3/*9     0   1   0   0   0
##   *4/*17    0   1   0   0   0
##   *4/*4     2   0   0   0   0
##   *4/*41    0   6   0   0   0
##   *4/*5     2   0   0   0   0
##   *4/*9     0   1   0   0   0
##   *41/*41   0   0   2   0   0
##   *5/*10    0   7   0   0   0
##   *5/*41    0   1   0   0   0
##   *5/*5     1   0   0   0   0
##   *5/*9     0   1   0   0   0
## 
##   *1  *10  *17   *3   *4  *41   *5   *6   *9 
## 73.3  9.8  3.3  0.5  5.9  3.6  2.4  0.1  1.2
##       
##          0 0.5   1 1.5   2
##   0      5   0   0   0   0
##   0.25   0   7   0   0   0
##   0.5    1  10  26   0   0
##   0.75   0   0   6   0   0
##   1      0   1  65   0   0
##   1.25   0   0   0  37   0
##   1.5    0   0   4  53   0
##   2      0   0   0   0 306
##                Activity Score
## CYP2D6 Genotype   0 0.25 0.5 0.75   1 1.25 1.5   2
##         *1/*1     0    0   0    0   0    0   0 306
##         *1/*10    0    0   0    0   0   37   0   0
##         *1/*17    0    0   0    0   0    0  25   0
##         *1/*3     0    0   0    0   0    0   3   0
##         *1/*4     0    0   0    0  46    0   0   0
##         *1/*41    0    0   0    0   0    0  19   0
##         *1/*5     0    0   0    0  12    0   0   0
##         *1/*6     0    0   0    0   0    0   1   0
##         *1/*9     0    0   0    0   0    0   9   0
##         *10/*10   0    0  26    0   0    0   0   0
##         *10/*41   0    0   0    6   0    0   0   0
##         *17/*17   0    0   0    0   3    0   0   0
##         *17/*41   0    0   0    0   2    0   0   0
##         *3/*4     0    0   1    0   0    0   0   0
##         *3/*9     0    0   0    0   1    0   0   0
##         *4/*17    0    0   1    0   0    0   0   0
##         *4/*4     2    0   0    0   0    0   0   0
##         *4/*41    0    0   6    0   0    0   0   0
##         *4/*5     2    0   0    0   0    0   0   0
##         *4/*9     0    0   1    0   0    0   0   0
##         *41/*41   0    0   0    0   2    0   0   0
##         *5/*10    0    7   0    0   0    0   0   0
##         *5/*41    0    0   1    0   0    0   0   0
##         *5/*5     1    0   0    0   0    0   0   0
##         *5/*9     0    0   1    0   0    0   0   0

Basic data plots

weight

##       country outcome7to180.1 outcome7to180.2
## 1      Brazil              32             155
## 2    Cambodia              58              19
## 3    Colombia              31              13
## 4    Ethiopia              21              28
## 5       India               0               9
## 6        Peru              28             167
## 7 Philippines               0               2
## 8    Thailand              15              59
## 9     Vietnam              14              29
##       country outcome7to180.1 outcome7to180.2
## 1      Brazil              42             233
## 2    Cambodia              69              29
## 3    Colombia              31              13
## 4    Ethiopia              33              42
## 5       India               4              51
## 6        Peru              39             298
## 7 Philippines              25               4
## 8    Thailand              25             138
## 9     Vietnam              14              29

## 
##   0  50 100 150 300 600 
## 186  55  57   1 481  57
## [1] 481
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## 
## Call:
## lm(formula = log10(day7_mthb) ~ tqmgkgtot + t_12_terminal_rescaled, 
##     data = pk_summaries)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.28670 -0.16433  0.00102  0.17727  0.60673 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             0.186896   0.054036   3.459 0.000579 ***
## tqmgkgtot               0.054259   0.005073  10.696  < 2e-16 ***
## t_12_terminal_rescaled -0.014094   0.002396  -5.883  6.5e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2555 on 635 degrees of freedom
##   (246 observations deleted due to missingness)
## Multiple R-squared:  0.2545, Adjusted R-squared:  0.2522 
## F-statistic: 108.4 on 2 and 635 DF,  p-value: < 2.2e-16

## [1] NA
## 
## Call:
## lm(formula = t_12_terminal ~ AS_score <= 0.5, data = outcome_dat)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.191 -2.315 -0.496  1.869 34.083 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          18.0725     0.2169  83.326   <2e-16 ***
## AS_score <= 0.5TRUE  -0.2942     0.7249  -0.406    0.685    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.092 on 389 degrees of freedom
##   (446 observations deleted due to missingness)
## Multiple R-squared:  0.0004231,  Adjusted R-squared:  -0.002146 
## F-statistic: 0.1647 on 1 and 389 DF,  p-value: 0.6851

Weight based models of tafenoquine efficacy

##               Estimate Std. Error   z value     Pr(>|z|)
## (Intercept) -1.0766413 0.56145372 -1.917596 5.516228e-02
## tqmgkgtot   -0.3167101 0.03578999 -8.849125 8.820840e-19
## logpara0     0.3393301 0.14151979  2.397757 1.649580e-02
## Using all data (n=836), the odds ratio for recurrence at 6 months for each additional mg/kg of tafenoquine is 0.73 (95% CI 0.68 to 0.78)
##               Estimate Std. Error    z value    Pr(>|z|)
## (Intercept)  0.3024656  0.8553941  0.3535979 0.723640219
## tqmgkgtot   -0.3718242  0.1158668 -3.2090673 0.001331663
## logpara0     0.1189197  0.1799389  0.6608894 0.508683256
## Using only patients who got a 300 mg dose (n=481), the odds ratio for recurrence at 6 months for each additional mg/kg of tafenoquine is 0.69 (95% CI 0.55 to 0.87)
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 5) + logpara0 + s(studysite, 
##     bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -2.2837     0.5979  -3.820 0.000134 ***
## logpara0      0.3403     0.1423   2.391 0.016807 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                edf Ref.df Chi.sq  p-value    
## s(tqmgkgtot)  1.00  1.001  78.84  < 2e-16 ***
## s(studysite) 16.48 21.000  54.20 6.95e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.181   Deviance explained = 16.7%
## UBRE = 0.12654  Scale est. = 1         n = 836
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 5) + logpara0 + s(studysite, 
##     bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  -1.6467     0.6851  -2.404   0.0162 *
## logpara0      0.1355     0.1830   0.740   0.4591  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                edf Ref.df Chi.sq p-value   
## s(tqmgkgtot) 1.710  2.137  9.553 0.00895 **
## s(studysite) 6.858 20.000 10.571 0.08467 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.0403   Deviance explained = 5.44%
## UBRE = 0.14752  Scale est. = 1         n = 481

Figure 1 of results in paper: the main mg/kg driving efficacy plot

## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot =
## xs_all, : factor levels -1 not in original fit
## Warning in predict.gam(mod_mgkg_sens_gam, newdata = data.frame(tqmgkgtot =
## xs_sens, : factor levels -1 not in original fit

compare logistic regression fits and spline fits

Compare for different parts of the world

## 
##       Africa     Americas Asia-Pacific 
##           42          544          251
## In Americas the odds ratio for recurrence for each additional mg/kg increase in tafenoquine dose is 0.73
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 3) + s(studysite, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -0.46618    0.09472  -4.922 8.58e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                    edf Ref.df Chi.sq p-value    
## s(tqmgkgtot) 1.000e+00      1  60.85  <2e-16 ***
## s(studysite) 4.724e-05      8   0.00   0.983    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.125   Deviance explained = 9.73%
## UBRE = 0.22299  Scale est. = 1         n = 544
## Warning in predict.gam(mod_rg, newdata = data.frame(tqmgkgtot = xs_all, : factor
## levels -1 not in original fit
## In Asia-Pacific the odds ratio for recurrence for each additional mg/kg increase in tafenoquine dose is 0.68
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 3) + s(studysite, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  -2.6509     0.9704  -2.732   0.0063 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                edf Ref.df Chi.sq  p-value    
## s(tqmgkgtot) 1.708  1.914  18.90 0.000962 ***
## s(studysite) 8.318 10.000  38.34 1.56e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.292   Deviance explained = 30.8%
## UBRE = -0.1382  Scale est. = 1         n = 251
## Warning in predict.gam(mod_rg, newdata = data.frame(tqmgkgtot = xs_all, : factor
## levels -1 not in original fit
## In Africa the odds ratio for recurrence for each additional mg/kg increase in tafenoquine dose is 0.73
## 
## Family: binomial 
## Link function: logit 
## 
## Formula:
## outcome7to180 ~ s(tqmgkgtot, k = 3) + s(studysite, bs = "re")
## 
## Parametric coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  -0.7729     0.4451  -1.736   0.0825 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Approximate significance of smooth terms:
##                 edf Ref.df Chi.sq p-value  
## s(tqmgkgtot) 1.0000      1  5.543  0.0186 *
## s(studysite) 0.3487      1  0.531  0.2174  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## R-sq.(adj) =  0.132   Deviance explained = 12.6%
## UBRE = 0.22433  Scale est. = 1         n = 42
## Warning in predict.gam(mod_rg, newdata = data.frame(tqmgkgtot = xs_all, : factor
## levels -1 not in original fit

## In patients who recived 300 mg single dose:
##         region outcome7to180
## 1       Africa          21.4
## 2     Americas          29.9
## 3 Asia-Pacific          20.3

Terminal elimination half-life and efficacy

##                 Estimate Std. Error   z value     Pr(>|z|)
## (Intercept)   -3.9267936 0.82687260 -4.748971 2.044547e-06
## t_12_terminal  0.1217612 0.02714725  4.485212 7.284163e-06
## logpara0       0.1756591 0.16250965  1.080915 2.797350e-01
## Using all data (n=836), the odds ratio for recurrence at 6 months for each additional mg/kg of tafenoquine is 0.73 (95% CI 0.68 to 0.78)

Power calculations and trial design

## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot =
## sim_dose_mgkg_current, : factor levels -1 not in original fit
## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot =
## sim_dose_mgkg_revised, : factor levels -1 not in original fit
## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot = 450/
## sim_ws[sim_ws >= : factor levels -1 not in original fit
## Warning in predict.gam(mod_mgkg_all_gam, newdata = data.frame(tqmgkgtot = 600/
## sim_ws[sim_ws >= : factor levels -1 not in original fit
## Under the current dosing the mean recurrence proportion within 6 months is 21.3%
## Under the revised dosing the mean recurrence proportion within 6 months is 7.9%
## If all adults>45 kg get 450 mg the mean recurrence proportion within 6 months is 11.9%
## If all adults>45 kg get 600 mg the mean recurrence proportion within 6 months is 6.2%

Power calculations

Superiority of 450 over 300 mg single dose